Crop growth and yield are determined by multiple factors, including genotype, environment, and their interactions. The assimilation of remote sensing data with crop growth modeling represents a significant trend for crop monitoring and yield estimation. This study aims to explore an effective data fusion method for estimating soybean yield by utilizing canopy remote sensing data and crop growth models. Based on field experiment data, remote sensing retrieval models for the leaf area index (LAI) and leaf nitrogen accumulation (LNA) were developed using the Principal Component Analysis–Ridge Regression (PCA–Ridge) algorithm. Using remotely sensed estimates as state variables in the DSSAT model, the results indicated that, compared with using only the LAI (V_LAI) or only LNA (V_LNA), the accuracy of soybean yield estimation was superior when both the LAI and LNA (V_LAI+LNA) were used as state variables. Additionally, the Nash–Sutcliffe efficiency (NSE) coefficient was a viable optimization function in optimizing the state variables. In conclusion, these results indicate that assimilating two key physiological and biochemical parameters for soybean, derived from hyperspectral data, with crop growth models provides a viable approach for enhancing the precision of estimating the LAI, LNA, and yield.

The middle reaches of the West Liaohe River Basin, a typical semi-arid to semi-humid transition and agro-pastoral ecotone in northern China, exhibit high ecological sensitivity, low resilience, and pronounced fragility. Despite growing concerns, existing studies in this region lack a comprehensive assessment paradigm that effectively couples inherent ecological attributes with nonlinear predictive modeling. To fill this gap, we developed an integrative framework that innovatively combined the SRP conceptual model with a stacking ensemble learning technique. This coupling is methodologically novel because it moves beyond linear assumptions, enables the detection of complex nonlinear response surfaces, and establishes a seamless analytical chain from historical evaluation to future projection. By selecting 13 indicators, including topography, climate, soil, vegetation, and socio-economic factors, the weight was determined by the comprehensive application of the analytic hierarchy process and entropy weight method, and the ecological fragility of the middle reaches of the West Liaohe River Basin from 2000 to 2020 was evaluated at multiple scales. The spatial differentiation driving factors were analyzed using a geographic detector. Therefore, an Ensemble Learning Regression model was used to simulate and predict the ecological fragility pattern in 2030. The results show that from 2000 to 2020, the ecological fragility of the study area showed a decreasing trend overall, with the Ecological Vulnerability Synthetical Index (EVSI) decreasing from 3.48 to 2.68, and the spatial pattern gradually shifting from “high in the northwest, low in the southeast” to “overall stability, local optimization.” The spatial agglomeration of ecological fragility gradually weakened, indicating that high-fragility areas tend to disperse and low-fragility areas expand in contiguous areas, and the ecosystem structure tends to develop towards equilibrium. The driving mechanism shows an evolution characteristic from “soil erosion dominated” to “biological abundance dominated,” with the impact of climate factors first increasing and then stabilizing, and the direct pressure from human activities continuously weakening. Under the assumption that historical trends continue, the ensemble learning model projects that by 2030, the ecological vulnerability pattern will be dominated by Mild and Moderate levels, with the area of extremely vulnerable regions significantly reduced to 0.36%. This study verified the applicability of the SRP model in transitional river basins, and the constructed “evaluation-driving mechanism-prediction” framework can provide a scientific basis for the ecological protection and adaptive management of the West Liaohe River Basin and provide a methodological reference for ecological fragility research in similar areas. However, limitations persist: the indicator system and weight assignment are subject to inherent subjectivity, and the 2030 scenario projection based on the Stacking ensemble learning model relies on the BAU (Business-As-Usual) assumption, which fails to account for abrupt climate extremes or major policy shifts. Future studies should incorporate multi-scenario constraints to reduce predictive uncertainty.

Against the backdrop of national policies promoting coordinated regional development and ecological civilization construction, the contradiction between ecological security and environmental carrying capacity in the Yangtze River Delta urban agglomeration has become increasingly prominent. However, the interaction mechanisms between these two systems remain insufficiently explored. This study constructs a comprehensive evaluation indicator system for ecological security and environmental carrying capacity in the Yangtze River Delta. A double exponential function is employed to examine the intensity of interaction pressure and reveal their nonlinear relationship. The coupling coordination model is applied to assess coordinated development trends, while a vector autoregression (VAR) model is used to identify the dynamic response relationships among system variables. The results indicate that the overall levels of both systems have improved, with core areas maintaining a leading position and southeastern, northeastern, and western regions remaining in a catching-up stage, accompanied by low-level convergence. Regional coordination exhibits a positive temporal evolution from imbalance to coordination, while its spatial pattern evolves from core dominance toward multi-regional convergence. Significant regional heterogeneity is observed in shock responses, with peripheral cities facing stronger industrial restructuring pressures showing greater ecological volatility. Overall, the dynamic interaction between ecological security and environmental carrying capacity demonstrates a stage-specific transition from mutual constraint to mutual promotion. This study provides empirical support for ecological restoration and regional sustainable development policymaking.